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\newcommand{\chinesethesistitle}{拒止环境多智能体系统构型协同控制与规划方法} %授权书用，无需断行
\newcommand{\englishthesistitle}{\uppercase{Configuration control and planning for multi-agent system in denied environments}} %\uppercase作用：将英文标题字母全部大写；
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\ctitle{拒止环境多智能体系统
	\\构型协同控制与规划方法}  %封面用论文标题，自己可手动断行
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\caffil{航天学院} %（在校生填所在系名称，同等学力人员填工作单位）
\cauthor{张 良}
\csupervisor{张泽旭 教授} %导师名字
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\eaffil{School of Astronautics}
\eauthor{Liang Zhang}                   %作者姓名 （英文）
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\BiAppendixChapter{摘~~~~要}{}
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\cabstract{

多智能体系统(Mulit-agent System, MAS)是由多个具备一定自主能力的计算单元所组成的复杂网络系统。由于具有成本低、鲁棒性强、效率高等优势，MAS目前正在逐渐替代人类，在极为苛刻的环境中执行危险的或单个智能体无法完成的复杂任务。拒止环境是一类导航、通信受限拒止的恶劣环境，是对军事对抗、外太空探索、深海探测等典型任务环境特征的归纳总结。拒止环境中复杂苛刻的外部条件对MAS内部的协同过程提出了更高的要求。在拒止环境中，MAS必须加强协作，促使更多功能的协同涌现。其中，构型的协同控制与规划是实现MAS功能涌现的重要手段，是MAS的基础研究问题之一。因此，需要对拒止环境下构型的协同控制和规划方法进行深入研究。

本文结合国防科技创新特区H863计划重点项目，对拒止环境下两类构型控制方法，以及两类构型规划方法，进行了进行深入研究。主要研究内容总结如下：

研究导航拒止环境下基于相对测量的包围构型控制方法。包围构型在对目标的协同观测、跟踪、定位等任务中发挥着重要作用。首先，明确定义了包围控制任务的期望构型；针对具有二阶运动学的MAS，以一维运动空间为例，提出了实现包围构型的分布式一致性加速度控制律；当跟随者无法获得绝对定位时，提出了自组织估计算法，仅使用相对测量估计跟随者与领导者中心的相对距离，解决了基于相对测量的包围构型控制问题。最后，二维平面的包围控制数值仿真验证了所提方法的正确性和有效性，分布式控制律具有抗节点损毁能力和较低的算法复杂度。

研究面向位置精度因子(Position Dilution of Precision, PDOP)定位构型的构型控制方法。MAS的协同定位过程和构型控制过程存在耦合效应，需要研究如何通过构型控制提升协同定位性能。针对MAS的移动定位系统，选择目标区域的PDOP分布作为衡量定位性能优劣的构型指标，建立了PDOP定位构型控制问题。设计了通用PDOP覆盖指标，解决了因为奇异构型而导致的传统PDOP在任务空间分布不连续的问题；根据覆盖控制框架，推导了覆盖指标对每一个多智能体节点位置状态的偏导数公式，设计了基于梯度下降的构型控制方法。最后通过三类场景的数值仿真，验证了构型控制方法的正确性和有效性。


研究基于连续信念空间的构型规划方法。拒止环境中存在各种不确定性。然而传统配置空间的规划方法没有考虑不确定性扰动，规划方案在执行时可能由于环境扰动而偏离预期效果。本文建立了基于高斯噪声的信念空间构型规划框架。研究了基于数值差分的目标函数梯度计算方法，提出了连续信念空间中基于梯度下降的待评估路径生成方法，避免了传统离散化路径生成方法具有的指数增长计算复杂度问题。最后通过主动协同定位的数值仿真实验，验证了所提出方法的有效性。理论分析和实验结果表明，所提构型规划方法可以抑制环境不确定性的影响，比传统方法具有更高的鲁棒性和较低的算法复杂度。


研究基于概率拓扑的构型规划方法。当传感器存在物理边界，且节点状态存在不确定性，系统的未来拓扑是时变且随机的。本文提出了基于概率拓扑的构型规划方法，精确计算每一个未来拓扑的分布概率，综合考虑所有未来拓扑的影响，突破了传统方法确定性测量拓扑的框架。设计了圆盘观测模型连通变量概率分布的精确计算算法，推导了算法稳定的充分条件。设计了基于概率拓扑的构型规划方法，以未来拓扑概率为权值，将所有拓扑分支的目标函数期望值作为评估指标。数值仿真实验表明，拓扑概率计算精度越高，构型规划方法对环境不确定性和时变随机拓扑的鲁棒性越强。

}

\ckeywords{多智能体系统；构型协同控制与规划；受限拒止环境；协同定位；不确定性规划；概率预测}

\eabstract{
Multi-agent system (or MAS) is a complex network composed of multiple single and intelligent units with certain autonomous capabilities. Due to the advantages of low cost, strong robustness, and high efficiency, MASs are currently gradually replacing humans, performing dangerous or complex tasks that a single agent cannot complete in extremely demanding environments. The denial environment is a type of harsh environment where the navigation and communication are restricted and denied. It is a summary of the environmental characteristics of typical missions such as military confrontation, outer space exploration, and deep-sea research. Those harsh external conditions in the environment put forward higher requirements for the collaborative process within the MAS. In the denied environment, the collaboration between agents must be strengthened to promote the emergence of more functional collaborations within the MAS. Because the cooperative control and planning of configuration is an important approach to realize the emergence of MAS, it is necessary to conduct further research on the cooperative control and planning methods of the MAS configuration in the denied environment.

Motivated by the necessities of MAS application in denied environment from real project, this dissertation further investigates two types of configuration control methods and two types of configuration planning methods for the MAS regarding the characteristics of the denied environment. The main research contents of this dissertation are as follows:

Research on the surrounding control problem of MAS using only relative measurements in the navigation denied environment. The surrounding formation plays an important role in the cooperative observation, tracking, positioning and other tasks of the MAS. The expected surrounding configuration is defined as a convex hull, which is centered at the leader’s geometrical center and has a radius bigger than the maximum distance between the leaders and geometrical center; Then the surrounding problem is investigated in the one-dimensional motion space considering second-order followers. A novel distributed acceleration control laws are proposed using absolute positions of followers. Then when the followers cannot obtain their absolute positions in the navigation denied environment, a self-organizing algorithm is designed for the estimation of the average distance between every follower and the leader's center based on relative measurements that are locally available in every follower. The final control protocol is derived by combining the acceleration control law with the estimation algorithm. Both theoretical analysis and simulation results validate the correctness and effectiveness. 

Research on the control problem of the Position Dilution of Precision (or PDOP) positioning configuration. There is a coupling effect between the cooperative localization (or CL) and the MAS configuration, which motivates the improvement of CL performance through configuration control. For the mobile positioning system composed of multiple agents, the PDOP distribution over target area is selected as the objective of configuration control to measure the positioning performance. Then the PDOP positioning configuration control problem is established. An improved PDOP coverage index is designed to solve the problem of discontinuous distribution of traditional PDOP caused by the singular MAS configurations. According to the coverage control framework, the analytical partial derivative of coverage objective over every single agent state is derived and a novel distributed gradient-descent configuration control law is proposed. Finally, numerical simulations verify the correctness and effectiveness of the proposed methods, showing that it can greatly expand the coverage area of positioning service by leveraging the mobility and coordination of MAS, and is robust to node loss/resurgence

Research on configuration planning problem based on continuous belief space. A variety of uncertainties exist in the denied environments due to both the noisy motion process and imperfect measurements. However, those uncertainties are not included in the configuration planning methods using deterministic states. Therefore, the solution generated from traditional methods may deviate from the expected performance due to the environmental disturbance during execution. Aiming at the above problems, a configuration planning framework using Gaussian parameterized belief space is established. The gradient of objective function is computed based on numerical difference, whereby a gradient-descent path planning in continuous belief space is proposed, which avoids the exponential complexity from the discretized methods. Finally, the numerical simulation verifies the effectiveness of the proposed method. Theoretical analysis and experimental results show that the proposed method can suppress the impact of environmental uncertainties and improve the robustness of planning methods. 

Research on configuration planning methods based on probabilistic topology. Aiming at the configuration planning problem where the observation sensor has a measurement boundary and the node state is uncertain, based on the previous belief space configuration planning method, we further release the usage of maximum likelihood observation for the prediction of future system behaviors within the planning horizon. The key of planning under probabilistic topology is to predict the probability of any observation connection for the disk model of sensors. Under Gaussian assumption, the distribution of a measurement connection can be modeled as the quadratic random variable in normal distribution and its probability can be computed by the finite-term approximation approach of the series expansion theorem. The truncation error of finite-term approximation is analyzed in both theoretical and experimental aspects. And a set of sufficient conditions for the stability of truncation error is summarized from these analyses. Finally, this method is applied to the configuration planning, and the configuration planning method under probabilistic topology is proposed. While the previous method only evaluates the future branch with maximum likelihood observation, the proposed planning method under probabilistic topology evaluates all possible branches and computes the expectation of objectives along all branches weighted by their corresponding probabilities. Therefore, the performance of configuration planning can be improved.

}

\ekeywords{Multi-Agent System, Configuration cooperative control and planning, Denied environment, Cooperative localization, Planning under uncertainty, Network probability prediction.}

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